There is increasing interest in the use of continuous housing systems for dairy cows, with various reasons put forward to advocate such systems. However, the welfare of dairy cows is typically perceived to be better within pasture-based systems, although such judgements are often not scientifically based. The aim of this review was to interrogate the existing scientific literature to compare the welfare, including health, of dairy cows in continuously housed and pasture-based systems. Although summarising existing work, knowledge gaps and directions for future research are also identified. The scope of the review is broad, examining relevant topics under three main headings; health, behaviour and physiology. Regarding health, cows on pasture-based systems had lower levels of lameness, hoof pathologies, hock lesions, mastitis, uterine disease and mortality compared with cows on continuously housed systems. Pasture access also had benefits for dairy cow behaviour, in terms of grazing, improved lying/resting times and lower levels of aggression. Moreover, when given the choice between pasture and indoor housing, cows showed an overall preference for pasture, particularly at night. However, the review highlighted the need for a deeper understanding of cow preference and behaviour. Potential areas for concern within pasture-based systems included physiological indicators of more severe negative energy balance, and in some situations, the potential for compromised welfare with exposure to unpredictable weather conditions. In summary, the results from this review highlight that there remain considerable animal welfare benefits from incorporating pasture access into dairy production systems.

The data set used in the present study was obtained from 20 energy metabolism studies involving 579 lactating dairy cows (511 Holstein-Friesian, 36 Norwegian Red, and 32 Jersey-Holstein crossbreds) varying in genetic merit, lactation number, stage of lactation, and live weight. These cows were offered diets based on grass silage (n=550) or fresh grass (n=29), and their energy intake and outputs, including methane energy (CH(4)-E), were measured in indirect open-circuit respiration calorimeter chambers. The objective was to use these data to evaluate relationships between CH(4)-E output and a range of factors in animal production and energetic efficiency in lactating dairy cows under normal feeding regimens. The CH(4)-E as a proportion of milk energy output (E(l)), E(l) adjusted to zero energy balance (E(l(0))), or intakes of gross energy (GE), digestible energy (DE), or metabolizable energy (ME) was significantly related to a wide range of variables associated with milk production (E(l) and E(l(0))) and energy parameters (energy intake, metabolizability, partitioning, and utilization efficiencies). Three sets of linear relationships were developed with experimental effects removed. The CH(4)-E/GE intake (r(2)=0.50-0.62) and CH(4)-E/E(l) (r(2)=0.41-0.68) were reduced with increasing feeding level, E(l)/metabolic body weight (MBW; kg(0.75)), E(l(0))/MBW, GE intake/MBW, DE intake/MBW, and ME intake/MBW. Increasing dietary ME/DE decreased CH(4)-E/E(l) (r(2)=0.46) and CH(4)-E/GE intake (r(2)=0.72). Dietary ME concentration and ME/GE were also negatively related to CH(4)-E/GE intake (r(2)=0.47). However, increasing heat production/ME intake increased CH(4)-E as a proportion of E(l) (r(2)=0.41), E(l(0)) (r(2)=0.67) and energy intake (GE, DE, and ME; r(2)=0.62 and 0.70). These proportional CH(4)-E variables were reduced with increasing ratios of E(l)/ME intake and E(l(0))/ME intake and efficiency of ME use for lactation (r(2)=0.49-0.70). Fitting CH(4)-E/E(l) or CH(4)-E/E(l(0)) against these energetic efficiencies in quadratic rather than linear relationships significantly increased r(2) values (0.49-0.67 vs. 0.59-0.87). In conclusion, CH(4)-E as a proportion of energy intake (GE, DE, and ME) and milk production (E(l) and E(l(0))) can be reduced by increasing milk yield and energetic efficiency of milk production or by reducing energy expenditure for maintenance. The selection of dairy cows with high energy utilization efficiencies and milk productivity offers an effective approach to reducing enteric CH(4) emission rates.

Although the effect of nutrition on enteric methane (CH4) emissions from confined dairy cattle has been extensively examined, less information is available on factors influencing CH4 emissions from grazing dairy cattle. In the present experiment, 40 Holstein-Friesian dairy cows (12 primiparous and 28 multiparous) were used to examine the effect of concentrate feed level (2.0, 4.0, 6.0, and 8.0 kg/cow per day; fresh basis) on enteric CH4 emissions from cows grazing perennial ryegrass-based swards (10 cows per treatment). Methane emissions were measured on 4 occasions during the grazing period (one 4-d measurement period and three 5-d measurement periods) using the sulfur hexafluoride technique. Milk yield, liveweight, and milk composition for each cow was recorded daily during each CH4 measurement period, whereas daily herbage dry matter intake (DMI) was estimated for each cow from performance data, using the back-calculation approach. Total DMI, milk yield, and energy-corrected milk (ECM) yield increased with increasing concentrate feed level. Within each of the 4 measurement periods, daily CH4 production (g/d) was unaffected by concentrate level, whereas CH4/DMI decreased with increasing concentrate feed level in period 4, and CH4/ECM yield decreased with increasing concentrate feed level in periods 2 and 4. When emissions data were combined across all 4 measurement periods, concentrate feed level (2.0, 4.0, 6.0, and 8.0 kg/d; fresh basis) had no effect on daily CH4 emissions (287, 273, 272, and 277 g/d, respectively), whereas CH4/DMI (20.0, 19.3, 17.7, and 18.1g/kg, respectively) and CH4-E/gross energy intake (0.059, 0.057, 0.053, and 0.054, respectively) decreased with increasing concentrate feed levels. A range of prediction equations for CH4 emissions were developed using liveweight, DMI, ECM yield, and energy intake, with the strongest relationship found between ECM yield and CH4/ECM yield (coefficient of determination = 0.50). These results demonstrate that offering concentrates to grazing dairy cows increased milk production per cow and decreased CH4 emissions per unit of milk produced.

Although interest in crossbreeding within dairy systems has increased, the role of Jersey crossbred cows within high concentrate input systems has received little attention. This experiment was designed to examine the performance of Holstein-Friesian (HF) and Jersey × Holstein-Friesian (J × HF) cows within a high concentrate input total confinement system (CON) and a medium concentrate input grazing system (GRZ). Eighty spring-calving dairy cows were used in a 2 (cow genotype) × 2 (milk production system) factorial design experiment. The experiment commenced when cows calved and encompassed a full lactation. With GRZ, cows were offered diets containing grass silage and concentrates [70:30 dry matter (DM) ratio] until turnout, grazed grass plus 1.0 kg of concentrate/day during a 199-d grazing period, and grass silage and concentrates (75:25 DM ratio) following rehousing and until drying-off. With CON, cows were confined throughout the lactation and offered diets containing grass silage and concentrates (DM ratio; 40:60, 50:50, 40:40, and 75:25 during d 1 to 100, 101 to 200, 201 to 250, and 251 until drying-off, respectively). Full-lactation concentrate DM intakes were 791 and 2,905 kg/cow for systems GRZ and CON, respectively. Although HF cows had a higher lactation milk yield than J × HF cows, the latter produced milk with a higher fat and protein content, so that solids-corrected milk yield (SCM) was unaffected by genotype. Somatic cell score was higher with the J × HF cows. Throughout lactation, HF cows were on average 37 kg heavier than J × HF cows, whereas the J × HF cows had a higher body condition score. Within each system, food intake did not differ between genotypes, whereas full-lactation yields of milk, fat plus protein, and SCM were higher with CON than with GRZ. A significant genotype × environment interaction was observed for milk yield, and a trend was found for an interaction with SCM. Crossbred cows on CON gained more body condition than HF cows, and overall pregnancy rate was unaffected by either genotype or management system. In summary, milk and SCM yields were higher with CON than with GRZ, whereas genotype had no effect on SCM. However, HF cows exhibited a greater milk yield response and a trend toward a greater SCM yield response with increasing concentrate levels compared with the crossbred cows.

Unbalanced metabolic status in the weeks after calving predisposes dairy cows to metabolic and infectious diseases. Blood glucose, IGF-I, non-esterified fatty acids (NEFA) and β-hydroxybutyrate (BHB) are used as indicators of the metabolic status of cows. This work aims to (1) evaluate the potential of milk mid-IR spectra to predict these blood components individually and (2) to evaluate the possibility of predicting the metabolic status of cows based on the clustering of these blood components. Blood samples were collected from 241 Holstein cows on six experimental farms, at days 14 and 35 after calving. Blood samples were analyzed by reference analysis and metabolic status was defined by k-means clustering (k=3) based on the four blood components. Milk mid-IR analyses were undertaken on different instruments and the spectra were harmonized into a common standardized format. Quantitative models predicting blood components were developed using partial least squares regression and discriminant models aiming to differentiate the metabolic status were developed with partial least squares discriminant analysis. Cross-validations were performed for both quantitative and discriminant models using four subsets randomly constituted. Blood glucose, IGF-I, NEFA and BHB were predicted with respective R 2 of calibration of 0.55, 0.69, 0.49 and 0.77, and R 2 of cross-validation of 0.44, 0.61, 0.39 and 0.70. Although these models were not able to provide precise quantitative values, they allow for screening of individual milk samples for high or low values. The clustering methodology led to the sharing out of the data set into three groups of cows representing healthy, moderately impacted and imbalanced metabolic status. The discriminant models allow to fairly classify the three groups, with a global percentage of correct classification up to 74%. When discriminating the cows with imbalanced metabolic status from cows with healthy and moderately impacted metabolic status, the models were able to distinguish imbalanced group with a global percentage of correct classification up to 92%. The performances were satisfactory considering the variables are not present in milk, and consequently predicted indirectly. This work showed the potential of milk mid-IR analysis to provide new metabolic status indicators based on individual blood components or a combination of these variables into a global status. Models have been developed within a standardized spectral format, and although robustness should preferably be improved with additional data integrating different geographic regions, diets and breeds, they constitute rapid, cost-effective and large-scale tools for management and breeding of dairy cows.